System, method, and program product for recognizing and rejecting fraudulent purchase attempts in e-commerce

Abstract

This disclosure relates generally to a system and method for using a machine-learning system to more accurately detect fraudulent use of credit cards on an e-commerce website and block those attempts.

What is claimed is: 
     
       1. A system for detecting and preventing distributed verification attacks on an e-commerce website having an API gateway for checkout, comprising:
 an e-commerce computer configured for connecting to a purchaser computer through the Internet; 
 a non-transitory computer readable medium containing a series of fraud-detection instructions that cause a website to:
 run a fraud detection webservice checking the validity of requests coming in from the e-commerce website and the API gateway; 
 wherein for each request coming in from the e-commerce website and the API gateway, the fraud detection webservice compares data about the user to a series of factors relevant to whether the purchase attempt is fraudulent and records the factors used to determine whether an attempt is fraudulent; 
 
 a server connected to the Internet, wherein the server contains programming directing the system to execute the fraud-detection instructions each time a user attempts to make a purchase; and 
 at least one machine learning algorithm for training the fraud detection system and adjusting the factors used to determine whether a distributed verification attack is taking place. 
 
     
     
       2. The system of  claim 1  wherein the factors used to determine whether a purchase attempt is fraudulent include information on a number of previous attempts made. 
     
     
       3. The system of  claim 1  wherein the factors used to determine whether a purchase attempt is fraudulent include a Customer account ID. 
     
     
       4. The system of  claim 1  wherein the factors used to determine whether a purchase attempt is fraudulent include an IP address. 
     
     
       5. The system of  claim 1  wherein the factors used to determine whether a purchase attempt is fraudulent include an amount of the transaction. 
     
     
       6. The system of  claim 1  wherein the factors used to determine whether a purchase attempt is fraudulent include a shipping address. 
     
     
       7. The system of  claim 1  wherein the factors used to determine whether a purchase attempt is fraudulent include products in carts. 
     
     
       8. The system of  claim 1  wherein the factors used to determine whether a purchase attempt is fraudulent include a Browser user agent. 
     
     
       9. The system of  claim 1  wherein the factors used to determine whether a purchase attempt is fraudulent include Browser language settings. 
     
     
       10. The system of  claim 1  wherein the factors used to determine whether a purchase attempt is fraudulent include an HTTP referrer. 
     
     
       11. The system of  claim 1  wherein the factors used to determine whether a purchase attempt is fraudulent include total time spent on the website. 
     
     
       12. The system of  claim 1  wherein the factors used to determine whether a purchase attempt is fraudulent include frequency of visits to the website. 
     
     
       13. The system of  claim 1  wherein the factors used to determine whether a purchase attempt is fraudulent include a ratio of successful orders to attempted orders. 
     
     
       14. The system of  claim 1  wherein the factors used to determine whether a purchase attempt is fraudulent include a number of pages visited by the user before checkout. 
     
     
       15. The system of  claim 1  also comprising a Checkout Action Aggregator that obtains data in context to an existing checkout request by referring to available historical data. 
     
     
       16. A method of detecting and preventing distributed verification attacks on an e-commerce website comprising a fraud filtering program, comprising:
 storing available historical data about customers and their purchases in a historical database; 
 comparing data stored in the historical database about a user attempting to complete a purchase on a website to a series of factors relevant to whether the purchase attempt is fraudulent; 
 using the data stored and the factors relevant to whether the purchase attempt is fraudulent to determine whether the purchase attempt is fraudulent; 
 recording the factors used to determine whether an attempt is fraudulent; 
 causing the website to execute the fraud filtering program each time a user attempts to make a purchase; 
 preventing the purchase from being completed if the attempt is deemed to be fraudulent; 
 sending the information on the factors used to determine whether an attempt is fraudulent to at least one machine learning algorithm to train the fraud filtering program and adjust the weights of factors used to determine whether an attempt is fraudulent; 
 using the recorded factors to train a system through machine-learning to better stop fraudulent attempts to use credit cards; and 
 incorporating the newly trained system into the fraud filtering program and adjusting the weight of the factors to determine whether an attempt is fraudulent in consequence of a distributed verification attack taking place. 
 
     
     
       17. The method of  claim 16  wherein the factors used to determine whether a purchase attempt is fraudulent include information on a number of previous attempts made. 
     
     
       18. The method of  claim 16  wherein the factors used to determine whether a purchase attempt is fraudulent include a Customer account ID. 
     
     
       19. The method of  claim 16  wherein the factors used to determine whether a purchase attempt is fraudulent include an IP address. 
     
     
       20. The method of  claim 16  wherein the factors used to determine whether a purchase attempt is fraudulent include an amount of the transaction, a shipping address, products in carts, a Browser user agent, Browser language settings, an HTTP referrer, total time spent on the website, frequency of visits to the website, a ratio of successful orders to attempted orders, or the number of pages visited by the user before checkout.